Water demand forecasting : a flexible approach
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Shabani, Sina
issued: 2018-09-30T00:00:00
issued: 2018
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Water distribution systems (WDS) operators would benefit greatly from educated estimates of water demand to help with the water scarcity threatening many regions worldwide. A wide range of stochastic and deterministic techniques have been proposed to model water demands in urban WDS. Every WDS needs a fair estimate of its future state for both development and operational purposes. Statistical models like time series forecasting and regression analysis have been widely used in the field of water demand forecasting. Recently, so-called artificial intelligence techniques became increasingly popular among scholars due to their high accuracy in prediction as well as not being limited to certain statistical assumptions. This research introduced gene expression programming (GEP), and support vector regression (SVM) and well-known artificial neural networks (ANN) as supervised learning algorithms for predictive analytics of water demand. K-means clustering as an unsupervised learning algorithm was tested to group the data based on a suitable distance metrics. The performance of the developed models was improved through phase space reconstruction of the time series data using optimum lag time determined by average mutual information. Monthly long-term water demand data of the City of Kelowna district (CKD) was used as the main case study throughout the research. Due to unavailability of water demand at finer short-term resolutions in Kelowna, the water demand data in the City of Milan was also used as a second case study for developing a framework of studying different temporal resolutions in the short-term analysis of water demand. Some scholars believe that predictive models are often wrong given the significant uncertainty in the conditions of underlying complex engineering systems. A novel technique based on data augmentation (data cropping and distorted data), and information theory were used to propose a flexible range of water demand (358 ML for upper bound and 335 ML…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/67223
note: WATER DEMAND FORECASTING: A FLEXIBLE APPROACH by Sina Shabani B.Sc., American University of Sharjah, 2011 M.Sc., American University of Sharjah, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) September 2018 © Sina Shabani, 2018 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: WATER DEMAND FORECASTING: A FLEXIBLE APPROACH submitted by Sina Shabani in partial fulfillment of the requirements of the degree of Doctor of Philosophy Dr. Gholamreza Naser, School of Engineering Supervisor Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Deborah Roberts, School of Engineering Supervisory Committee Member Dr. Jeff Curtis, School of Engineering University Examiner Dr. Yves Filion, School of Engineering and Applied Science, Queen’s University External Examiner iii Abstract Water distribution systems (WDS) operators would benefit greatly from educated estimates of water demand to help with the water scarcity threatening many regions worldwide. A wide range of stochastic and deterministic techniques have been proposed to model water demands in urban WDS. Every WDS needs a fair estimate of its future state for both development and operational purposes. Statistical models like time series forecasting and regression analysis have been widely used in the field of water demand forecasting. Recently, so-called artificial intelligence techniques became increasingly popular among scholars due to their high accuracy in prediction as well as not being limited to certain statistical assumptions. This research introduced gene expression programming (GEP), and support vector regression (SVM) and well-known artificial neural networks (ANN) as supervised learning algorithms for predictive analytics of water dem…
has type: Thesis/Dissertation
date issued: 2018-11
is shown at: 10.14288/1.0372099
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution 4.0 International
rights URI: http://creativecommons.org/licenses/by/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/67223